Analysis of Potential Partner Characteristics

Language Heterogeneity

Clearly this shows a lot of diversity in language among Asian ethnic groups. It also reveals that the general language codes may be a bit problematic for a few Asian groups. I probably need to think more carefully about the language variable I use to define language endogamy. I probably can use the detailed language codes, but I may need to look at particular cases that need collapsing. This will take some work. Also, what is up with Hmong being listed as Tibetan? I checked and that seems accurate in the data.

Mostly Hindi (and related) or Dravidian languages in South Asia, but I wonder if I need to go into more detail here for those language groups in languaged?

For Latinos, much less diversity. The only real diversity is given by the percentage of each group that speak English vs. Spanish.

Birthplace Heterogeneity

This looking much better using the detailed birthplace codes. Mongolia is still a bit of an issue, but if you look at the actual code its “Other Asia, NEC” so not much that can be done with it.

In general, we see a lot of diversity here as one would expect. mostly the ethnic group corresponds pretty closely to a common birthplace, because of the implied nationality, but there are several cases where this is not true.

Mostly it ties closely to nationality, with pretty similar percentages born in the US. The Asian Indian population is more diverse in birthplace than other groups, partly reflecting transnational migration in the British Empire, I suspect.

Summary Diversity Measure

Another approach to this issue is to use the formula:

\[1-\sum p_i^2\] Where \(i\) is a given category of a variable and \(p_i\) is the proportion of the population that belongs to the given category. This measure is basically the Simpson index but reversed to indicate diversity rather than concentration (I believe this is the Gini-Simpson index). The measure gives the probability that two randomly selected individuals from a given population will belong to different categories. I can use this to calculate a language and birthplace diversity measure for each race and pan-ethnic group. I am going to calculate this for the three separate pan-ethnic groups and also calculate it within each specific ethnic group.

The Gibbs–Martin index of sociology, psychology and management studies,[13] which is also known as the Blau index, is the same measure as the Gini–Simpson index.

Race Reporting among Latinos

Dominicans and Puerto Ricans are among the groups most likely to identify as black. The other two groups with high black reporting are Panamanians and Costa Ricans. Panamanians are much higher than all other categories, but unfortunately are too small a group to include in the analysis.

Dominicans are also the least likely to identify as white alone. Puerto Ricans are mor middling in this regard.

Overall Ethnic Exogamy

I begin by looking at the models that estimate a single term for Asian, Latino, and South Asian ethnic exogamy. These terms estimate the (log) odds of an ethnically exogamous union in comparison to an endogamou union. I want to see how these estimates change over time and with controls for birthplace and language endogamy. To look at the effects of controls, I run four sets of models in each time period:

  1. A baseline model that controls for the age difference between the spouses (and its square), educational crossing parameters, and hypogamy/hypergamy terms.
  2. A model that adds to the baseline model a control for language endogamy.
  3. A model that adds to the baseline model a control for birthplace endogamy.
  4. A model that adds to the baseline model both controls for language and birthplace endogamy.

These models are estimated on data that is restricted to the ethnic groups available in the Census 1980 data (Mexican, Cuban, Puerto Rican, Chinese, Japanese, Korean, Vietnamese, and Filipino). This allows for results that are directly comparable across time. However, I also want to see how these results change if I add in all of the possible Asian and Latino nationality groups in the later time period. This gives me 19 Latino groups and 15 Asian groups. Using this data, I also have three additional South Asian groups (Bangladeshi, Pakistani, and Sri Lankan) and so I can estimate an ethnic exogamy term for this group.

E&SE Asian Ethnic Exogamy

The results for Asian ethnic exogamy reveal that language and birthplace endogamy both play important roles. Without controlling for either one, the results show that Asian ethnic exogamy is moderate and has actually gone down a little over time. However, once we factor out language and birthplace endogamy, ethnic exogamy is quite common and has increased substantially over time. Language endogamy is more important than birthplace endogamy, but both play a role.

Thus for Asian nationality groups, we actually observe relatively little pan-ethnicity in intermarriage, but that is because these groups are divided by language and birthplace diversity. In a counterfactual case in which most Asian Americans are all born in the US and speak English, we would see high level of ethnic exogamy.

If I expand the analysis in the later data to all Asian ethnic groups, the results are very similar, with slightly higher ethnic exogamy in each model.

The figure above shows the Asian ethnic exogamy term in comparison to the terms for Asian outmarriage with other racial groups. A few features stand out:

  • Asian ethnic exogamy is more common than any form of racial exogamy and has increased over time.
  • Asian/South Asian exogamy is much less common than overall Asian ethnic exogamy and has actually become slightly less common over time.
  • Of the four interracial marriage possibilities here, Asian/White intermarriage has increased the most, but still is much less likely than Asian ethnic exogamy.
  • Asian/Black intermarriage remains extremely rare and has hardly changed at all.

Latino Ethnic Exogamy

For Latino ethnic exogamy we see a very different story. Using the three Latino groups available in Census 1980, there have not been significant increases in Latino ethnic exogamy over time. In fact, Latino ethnic exogamy has decreased slightly in the baseline models. Controlling for birthplace and language endogamy has relatively little effect on these patterns. Controlling for birthplace endogamy increases exogamy somewhat, as expected. Controlling for language endogamy by itself has very little effect on the model, except for the slightest of increases in the ACS data. In models that control for both forms of endogamy, we do see a very slight increase in ethnic exogamy over time, but the change is very small.

If we expand to the full 19 Latino groups in the ACS data, the patterns are somewhat similar, except that controlling for language endogamy actually decreases ethnic exogamy slightly. There is also greater Latino ethnic exogamy overall when we expand. We cannot really say if this is an increase because we don’t have a comparison for 1980, but it does suggest that the particular groups of Mexicans, PRs and Cubans are somewhat less exogamous with each other than Latinos overall.

The figure above shows where Latino ethnic exogamy stands in relation to racial outmarriage among Latinos. The results here are somewhat different than the Asian case. Notably:

  • There has been a large increase in the odds of intermarriage between Latinos and Whites and relatively little change in Latino ethnic exogamy. The consequence is that in the restricted group case, Latino/White intermarriage is slightly more likely than Latino ethnic exogamy in the current data. In the data with more ethnic groups, Latino ethnic exogamy is still slightly more common than Latino/White intermarriage.
  • Latino/Black intermarriage remains very rare but has increased substantially over time. This suggests somewhat weaker boundaries at this “black/non-black” divide for Latinos than for Asians. Some of this may be due to racial heterogeneity among Latinos (i.e. Afro-Latinos).
  • The South Asian/Latino parameter has gone down significantly. This seems to be a general pattern for the South Asian term and may reflect measurement issues.

South Asian Ethnic Exogamy

I can only look at South Asian ethnic exogamy in the ACS data with all ethnic groups, which has the groups of Asian Indian, Pakistani, Bangladeshi, and Sri Lankan. In all other cases, the only South Asian ethnic group is Asian Indians.

the results here are quite similar to the E&SE Asian case. The baseline ethnic exogamy is even higher and the increases with birthplace and language endogamy controls put ethnic exogamy at almost the exact same value as for E&SE Asians. It suggests two “ethnic” melting pots for Asians.

The figure above looks at South Asian racial exogamy. I also include ethnic exogamy here for comparative purposes with the same dataset, but I cannot look at it over time due to the lack of comparable data across time.

The results show little difference in the ACS data with using only Asian Indians and using all four groups. In all cases, South Asian racial intermarriage has become less likely over time.

I do worry that these drops are driven by measurement issues. The Census lists “Asian Indian” as a category to help distinguish it from the case where people are looking for an American Indian category. My guess would be that people have gotten better over time at not mis-checking this box when they meant to select an American Indian category. Those mischeckers are probably more likely to intermarry with other groups given the historical legacy of the American Indian group. Thus, the later period may be witnessing a drop simply because it is more accurately capturing people who intended to check Asian Indian. In support of this hypothesis, the odds of intermarriage between American Indian and Asian Indian respondents is extremely high in 1980 and diminishes dramatically in the ACS data.

Ethnic-Group Specific Exogamy

I now turn to models that estimate full ethnicity-by-ethnicity terms within the E&SE Asian and Latino categories. These terms will allow me to create heat maps with combined dendrograms that show the patterns of association between specific ethnic groups within these big race categories.

The 15 E&SE Asian categories, 19 Latino categories, and 4 South Asian categories are unfeasible for an analysis for two reasons. First, the sample size of some of these groups is so small that I end up with sparse data and models that do not fully converge on parameters involving these groups. Even in cases, with convergence, the standard errors are so large that I cannot draw any conclusions with precision. Second, the number of parameters that I would need to estimate for these models is ridiculous (282 ethnic-by-ethnic terms in the fullest model) making the models impossible to actually fit.

To determine what is feasible to fit, I used basic cutoffs on group size to eliminate groups from model estimation, until I produced models that fit well on all parameters with standard errors that give reasonable precision. This was an iterative process.

For the E&SE Asian case, I ultimately was forced to use the same five groups as in the 1980 data:

I hade hoped to also use the SE Asian groups of Thai, Hmong, and Cambodian, all of whom have similar sample sizes. However, the standard errors on these estimates were very large and a few of the parameters between these groups failed to converge.

For the Latino case, I was able to retain the nine largest groups of:

This gives me a good sampling of Central American, Caribbean, and South American nationalities. It also gives me two groups (PR and Dominican) that are generally considered more afro-Latino than other groups (although Colombians and Cubans maybe as well?).

I also included specific variables for Black/Latino ethnicity and Black/Asian ethnicity rather than single Black/Latino and Black/Asian variables, respectively. This approach allows me to see whether the Black/non-Black divide works differently for some Latino and Asian ethnic groups.

For South Asians, none of the groups outside of Asian Indians are large enough to sustain an analysis.

In addition to all of these parameters, I also include a Filipino/Latino dummy variable to capture particular affinities between Latinos and this “latinized” Asian ethnic group.

E&SE Asian Specific Ethnic Exogamy

I start by looking at heat maps of the odds ratio of endogamy when looking at specific combinations of ethnic groups. I also treat these odds ratios as distances in order to calculate dendrograms showing the closeness of each ethnic group. The figures below show both the heat map and dendrograms. I show the heat map for each of the four models to see how controlling for laanguage and birthplace endogamy affect the results.

In general, here is my summary of what I see:

  • Language and birthplace endogamy both reduce these odds ratios quite a bit although language endogamy plays a bigger role. They also shift the relationships among groups somewhat.
  • The biggest division is between Filipinos and all others. There remain significant boundaries with every other ethnic group included even after controlling for language and birthplace endogamy. This barrier seems to be a bit weaker for Vietnamese/Filipino unions than other cases.
  • For the remaining categories, the Chinese category operates like a bridging category – there are no barriers to intermarriage with Chinese partners at all for the remaining groups.
  • Little evidence of boundaries between Korean and Japanese intermarriage, but some boundaries between these two groups and Vietnamese.

Changes over time

The results here show increases in all cases of ethnic exogamy, although the largest increases are for the East Asian groups of Chinese, Japanese, and Korean. In every case of these latter groups, the estimated parameters have crossed the important threshold of one.

Latino Specific Ethnic Exogamy

I run the same heatmaps for Latino exogamy.

With more groups, the results here are more complicated to interpret, but here is what I see:

  • I see some evidence of regional divisions. The Central American/Mexican cases all group together. The South American and Caribbean cases also sort of group together, although Cubans don’t quite fit here. They are about equally distant from South American and Caribbean groups.
  • The odds between Puerto Rican and Dominican are the second lowest and both of these groups are the “blackest” so there may be something there.

Changes over time

There have been litte change over time in specific latino ethnic exogamy among the three groups where we can track change. Some evidence of slight increases for Cuban/Puerto Rican and Cuban/Mexican, but none for Mexican/Puerto Rican.

Interracial Marriage by Ethnicity

I look specifically here at the odds of Asian and Latino intermarriage with whites and blacks by ethnic group.

Latino Interracial Marriage

Significant variation in intermarriage with whites by Latino ethnic group. Intermarriage with blacks however basically falls into two groups: Dominicans and Puerto Ricans vs. everyone else.

How do these odds compare to the odds of Latino ethnic exogamy for Puerto Ricans and Dominicans?

The odds of outmarrying to a Black partner are still lower than exogamy to most other Latino ethnic groups although they are relatively comparable for many cases.

In 1980, the odds for all three groups were very similar. Black/Puerto Rican intermarriage has increased substantially over time, in contrast to the other cases. Evidence of increasing racialization of Puerto Ricans (and probably Dominicans) as black over time.

Asian Interracial Marriage

Less variation in both forms of exogamy for Asian ethnic groups than for Latino groups. Odds of intermarriage with blacks is much lower than with whites. White exogamy is lower than for Latinos.

Not a lot of change over time. The story is one of consistently low odds suggesting a very strong and persistent boundary between Asian ethnicities and Blackness.

Filipino/Latino Exogamy

Filipino/Latino exogamy is considerably higher than E&SE Asian/Latino exogamy overall.

Comparison of Birthplace Endogamy Specifications

For the birthplace endogamy variable, I had to consider how to treat the “1.5” generation - those born abroad but who migrated to the US as children. I followed previous research which has split this group into a “1.75” generation of children who migrated aged 0-5 and thus have lived most of their lives in the US, a “1.5” generation that migrated aged 6-12 and so have some experiences in both countries and a “1.25” generation who migrated aged 13-17 and thus spent most of their formative years in the country of origin.

For each of these groups, I considered three possibilities for coding birthplace:

With three groups and three options, there are 27 possible options for how to code this variable. However, I only accepted combinations where order of the generations matched the order of assimilation. So you could not have a case where the 1.75 generation is given “Birthplace” coding and the other two groups are given the more assimilative options of “USA” or “Both.” This left me with 10 possible options.

I created dummy variables for birthplace endogamy based on these 10 options and then ran a model on the ACS and Census 1980 data with each dummy variable. I used the ACS data with the fullest set of ethnic groups, racial pentagon coding, and controls for age, educational assortative mating, and language endogamy. The results below show the deviance for each of the ten models, ranked from lowest deviance to highest deviance for the ACS data.

Deviance from models using different specifications of birthplace for 1.25, 1.5, and 1.75 generation
model gen1.75 gen1.5 gen1.25 deviance_census deviance_acs
all_first Birthplace Birthplace Birthplace 1122736 1850594
partial_flex1.75 Both Birthplace Birthplace 1122758 1849753
steep_grade1.75 USA Birthplace Birthplace 1122856 1850491
partial_flex1.5 Both Both Birthplace 1122775 1849202
full_grade USA Both Birthplace 1122851 1849714
steep_grade1.5 USA USA Birthplace 1123002 1851089
all_flex Both Both Both 1122932 1850676
slight_grade1.75 USA Both Both 1122968 1850894
slight_grade1.5 USA USA Both 1123013 1851576
all_second USA USA USA 1122968 1852855

For both time periods, the least preferred option were those coded as USA for most cases. In both time periods, both models preferred “Birthplace” for the 1.25 generation. The best fitting model for the ACS data uses the “Both” coding for both the 1.75 and the 1.5 generation, while the best fitting model for the Census data uses the “Birthplace” coding for all three groups.

The differences in deviance in the Census data are much smaller than in the ACS data, indicating that the preferences by model are not as strong. The strongest fitting model in the ACS data is the third strongest model in the Census data. Nonetheless, I should probably use the best-fitting model for each year in the actual analysis.

Full Model Output

Census 1980

Model predicting union formation on Census 1980 data using racial pentagon categories
  base bendog lendog both
agediff 0.136*** 0.136*** 0.136*** 0.136***
  (0.001) (0.001) (0.001) (0.001)
agediff^2 -0.024*** -0.024*** -0.024*** -0.024***
  (0.000) (0.000) (0.000) (0.000)
hypergamyTRUE 0.442*** 0.442*** 0.446*** 0.446***
  (0.010) (0.010) (0.011) (0.011)
hypogamyTRUE 0.273*** 0.273*** 0.275*** 0.275***
  (0.013) (0.013) (0.014) (0.014)
edcross_hsTRUE -1.041*** -1.037*** -1.038*** -1.036***
  (0.011) (0.011) (0.012) (0.012)
edcross_scTRUE -1.111*** -1.111*** -1.112*** -1.112***
  (0.008) (0.009) (0.009) (0.009)
edcross_cTRUE -1.004*** -1.004*** -1.004*** -1.004***
  (0.011) (0.011) (0.011) (0.011)
race_exog_pentAIAN.E&SE Asian -2.300*** -2.157*** -1.796*** -1.749***
  (0.288) (0.290) (0.284) (0.285)
race_exog_pentAIAN.Latino -2.299*** -2.250*** -1.597*** -1.595***
  (0.096) (0.096) (0.101) (0.101)
race_exog_pentAIAN.South Asian -0.103 0.214 0.507 0.640
  (0.697) (0.708) (0.667) (0.675)
race_exog_pentBlack.AIAN -3.315*** -3.318*** -3.146*** -3.152***
  (0.144) (0.144) (0.146) (0.146)
race_exog_pentBlack.E&SE Asian -3.830*** -3.666*** -3.379*** -3.322***
  (0.140) (0.141) (0.145) (0.145)
race_exog_pentBlack.Latino -3.914*** -3.826*** -3.251*** -3.231***
  (0.059) (0.059) (0.058) (0.058)
race_exog_pentBlack.South Asian -3.691*** -3.379*** -3.111*** -2.979***
  (0.454) (0.448) (0.433) (0.430)
race_exog_pentE&SE Asian.E&SE Asian -1.245*** -1.075*** -0.741*** -0.683***
  (0.128) (0.128) (0.131) (0.130)
race_exog_pentE&SE Asian.South Asian -2.051** -1.738* -1.339 -1.217
  (0.700) (0.685) (0.725) (0.714)
race_exog_pentLatino.E&SE Asian -2.685*** -2.509*** -1.963*** -1.906***
  (0.107) (0.109) (0.118) (0.117)
race_exog_pentLatino.Latino -1.422*** -1.244*** -1.372*** -1.278***
  (0.072) (0.073) (0.078) (0.079)
race_exog_pentLatino.South Asian -2.367*** -2.051*** -1.479*** -1.354***
  (0.385) (0.387) (0.395) (0.397)
race_exog_pentWhite.AIAN -1.436*** -1.437*** -1.244*** -1.249***
  (0.043) (0.044) (0.043) (0.043)
race_exog_pentWhite.Black -4.764*** -4.761*** -4.761*** -4.760***
  (0.033) (0.033) (0.033) (0.033)
race_exog_pentWhite.E&SE Asian -2.170*** -2.019*** -1.712*** -1.663***
  (0.057) (0.057) (0.056) (0.055)
race_exog_pentWhite.Latino -2.338*** -2.273*** -1.701*** -1.690***
  (0.020) (0.020) (0.021) (0.021)
race_exog_pentWhite.South Asian -1.792*** -1.467*** -1.197*** -1.062***
  (0.200) (0.193) (0.182) (0.179)
bendog_all_firstTRUE   0.481***   0.239***
    (0.021)   (0.022)
language_endogTRUE     1.184*** 1.145***
      (0.017) (0.017)
deviance 1132164.158 1130913.271 1123022.149 1122735.882
***p < 0.001; **p < 0.01; *p < 0.05
Model predicting union formation on Census 1980 data using specific Asian and Latino ethnic categories
  base bendog lendog both
agediff 0.136*** 0.136*** 0.136*** 0.136***
  (0.001) (0.001) (0.001) (0.001)
agediff^2 -0.024*** -0.024*** -0.024*** -0.024***
  (0.000) (0.000) (0.000) (0.000)
hypergamyTRUE 0.443*** 0.442*** 0.446*** 0.446***
  (0.011) (0.011) (0.011) (0.011)
hypogamyTRUE 0.273*** 0.273*** 0.275*** 0.275***
  (0.013) (0.013) (0.014) (0.014)
edcross_hsTRUE -1.041*** -1.037*** -1.038*** -1.036***
  (0.011) (0.011) (0.012) (0.012)
edcross_scTRUE -1.111*** -1.111*** -1.112*** -1.112***
  (0.009) (0.009) (0.009) (0.009)
edcross_cTRUE -1.004*** -1.004*** -1.004*** -1.004***
  (0.011) (0.010) (0.011) (0.011)
race_exog_extendedAIAN.E&SE Asian -2.261*** -2.114*** -1.773*** -1.722***
  (0.306) (0.309) (0.290) (0.291)
race_exog_extendedAIAN.Latino -2.293*** -2.246*** -1.598*** -1.597***
  (0.096) (0.096) (0.101) (0.101)
race_exog_extendedAIAN.South Asian -0.104 0.215 0.503 0.642
  (0.698) (0.708) (0.667) (0.675)
race_exog_extendedBlack.AIAN -3.321*** -3.325*** -3.154*** -3.160***
  (0.144) (0.143) (0.146) (0.145)
race_exog_extendedBlack.Chinese -4.570*** -4.428*** -3.909*** -3.866***
  (0.290) (0.292) (0.292) (0.293)
race_exog_extendedBlack.Cuban -4.145*** -3.823*** -3.313*** -3.179***
  (0.245) (0.245) (0.261) (0.261)
race_exog_extendedBlack.Filipino -3.569*** -3.305*** -3.062*** -2.957***
  (0.264) (0.265) (0.264) (0.265)
race_exog_extendedBlack.Japanese -3.701*** -3.630*** -3.545*** -3.515***
  (0.277) (0.279) (0.279) (0.280)
race_exog_extendedBlack.Korean -2.869*** -2.512*** -2.270*** -2.116***
  (0.355) (0.357) (0.370) (0.369)
race_exog_extendedBlack.Mexican -4.296*** -4.248*** -3.709*** -3.704***
  (0.087) (0.087) (0.091) (0.091)
race_exog_extendedBlack.Puerto Rican -3.484*** -3.346*** -2.648*** -2.601***
  (0.114) (0.116) (0.119) (0.119)
race_exog_extendedBlack.South Asian -3.696*** -3.382*** -3.116*** -2.979***
  (0.457) (0.451) (0.435) (0.432)
race_exog_extendedChinese.Filipino -1.898*** -1.664*** -1.215*** -1.122***
  (0.303) (0.308) (0.303) (0.306)
race_exog_extendedChinese.Japanese -0.661*** -0.566** -0.256 -0.226
  (0.194) (0.199) (0.186) (0.188)
race_exog_extendedChinese.Korean -1.239* -1.006 -0.491 -0.401
  (0.546) (0.556) (0.562) (0.566)
race_exog_extendedChinese.White -2.833*** -2.698*** -2.165*** -2.125***
  (0.147) (0.148) (0.149) (0.149)
race_exog_extendedCuban.Mexican -1.836*** -1.538*** -1.759*** -1.602***
  (0.194) (0.197) (0.198) (0.197)
race_exog_extendedCuban.Puerto Rican -1.389*** -1.077*** -1.381*** -1.220***
  (0.161) (0.162) (0.169) (0.174)
race_exog_extendedCuban.White -2.083*** -1.757*** -1.246*** -1.111***
  (0.101) (0.098) (0.105) (0.103)
race_exog_extendedE&SE Asian.South Asian -2.049** -1.724** -1.328 -1.195
  (0.673) (0.658) (0.705) (0.696)
race_exog_extendedFilipino.Japanese -2.149*** -1.941*** -1.680*** -1.597***
  (0.261) (0.264) (0.269) (0.270)
race_exog_extendedFilipino.Korean -2.188** -1.862* -1.422 -1.287
  (0.793) (0.798) (0.800) (0.802)
race_exog_extendedFilipino.White -2.107*** -1.847*** -1.561*** -1.459***
  (0.130) (0.133) (0.127) (0.132)
race_exog_extendedJapanese.Korean -0.512 -0.276 -0.006 0.091
  (0.455) (0.442) (0.465) (0.456)
race_exog_extendedJapanese.White -1.679*** -1.630*** -1.566*** -1.547***
  (0.079) (0.078) (0.075) (0.075)
race_exog_extendedKorean.White -1.715*** -1.371*** -1.075*** -0.928***
  (0.171) (0.169) (0.179) (0.174)
race_exog_extendedLatino.E&SE Asian -2.959*** -2.799*** -2.229*** -2.177***
  (0.148) (0.150) (0.155) (0.155)
race_exog_extendedLatino.South Asian -2.382*** -2.063*** -1.486*** -1.354***
  (0.396) (0.397) (0.399) (0.399)
race_exog_extendedMexican.Puerto Rican -1.468*** -1.312*** -1.267*** -1.185***
  (0.111) (0.109) (0.139) (0.133)
race_exog_extendedMexican.White -2.222*** -2.191*** -1.651*** -1.654***
  (0.020) (0.020) (0.022) (0.022)
race_exog_extendedPuerto Rican.White -2.961*** -2.836*** -2.116*** -2.076***
  (0.077) (0.078) (0.083) (0.082)
race_exog_extendedWhite.AIAN -1.435*** -1.436*** -1.244*** -1.250***
  (0.043) (0.044) (0.043) (0.043)
race_exog_extendedWhite.Black -4.768*** -4.766*** -4.766*** -4.765***
  (0.033) (0.033) (0.033) (0.033)
race_exog_extendedWhite.South Asian -1.794*** -1.467*** -1.201*** -1.058***
  (0.201) (0.193) (0.181) (0.178)
race_filipino_latinoTRUE 0.750** 0.844*** 0.757** 0.803***
  (0.235) (0.241) (0.232) (0.234)
bendog_all_firstTRUE   0.483***   0.249***
    (0.022)   (0.023)
language_endogTRUE     1.177*** 1.138***
      (0.017) (0.018)
deviance 1131615.603 1130384.491 1122649.349 1122345.979
***p < 0.001; **p < 0.01; *p < 0.05

ACS 2014-18

Model predicting union formation on ACS 2014-18 data using racial pentagon categories and Asian/Latino ethnic groups available in 1980
  base bendog lendog both
agediff 0.055*** 0.055*** 0.055*** 0.055***
  (0.000) (0.000) (0.000) (0.000)
agediff^2 -0.013*** -0.013*** -0.013*** -0.013***
  (0.000) (0.000) (0.000) (0.000)
hypergamyTRUE -0.128*** -0.137*** -0.131*** -0.135***
  (0.008) (0.008) (0.008) (0.008)
hypogamyTRUE 0.230*** 0.224*** 0.224*** 0.222***
  (0.009) (0.009) (0.009) (0.009)
edcross_hsTRUE -0.770*** -0.713*** -0.725*** -0.698***
  (0.010) (0.010) (0.010) (0.010)
edcross_scTRUE -0.667*** -0.657*** -0.659*** -0.654***
  (0.007) (0.007) (0.007) (0.007)
edcross_cTRUE -0.834*** -0.829*** -0.825*** -0.824***
  (0.007) (0.007) (0.007) (0.007)
race_exog_pentAIAN.E&SE Asian -2.842*** -2.580*** -2.260*** -2.163***
  (0.183) (0.184) (0.183) (0.183)
race_exog_pentAIAN.Latino -2.072*** -1.951*** -1.495*** -1.468***
  (0.050) (0.050) (0.051) (0.050)
race_exog_pentAIAN.South Asian -3.047*** -2.759*** -2.398*** -2.267***
  (0.433) (0.429) (0.436) (0.432)
race_exog_pentBlack.AIAN -2.556*** -2.537*** -2.453*** -2.449***
  (0.099) (0.099) (0.100) (0.100)
race_exog_pentBlack.E&SE Asian -3.541*** -3.257*** -2.924*** -2.819***
  (0.060) (0.060) (0.061) (0.061)
race_exog_pentBlack.Latino -2.682*** -2.516*** -2.064*** -2.019***
  (0.022) (0.022) (0.024) (0.024)
race_exog_pentBlack.South Asian -4.152*** -3.830*** -3.589*** -3.436***
  (0.128) (0.129) (0.132) (0.132)
race_exog_pentE&SE Asian.E&SE Asian -1.378*** -1.016*** -0.531*** -0.387***
  (0.049) (0.049) (0.048) (0.049)
race_exog_pentE&SE Asian.South Asian -3.003*** -2.575*** -2.141*** -1.946***
  (0.105) (0.110) (0.120) (0.123)
race_exog_pentLatino.E&SE Asian -2.674*** -2.357*** -1.795*** -1.677***
  (0.035) (0.035) (0.037) (0.037)
race_exog_pentLatino.Latino -1.514*** -1.266*** -1.331*** -1.162***
  (0.034) (0.034) (0.035) (0.035)
race_exog_pentLatino.South Asian -3.451*** -3.099*** -2.567*** -2.415***
  (0.097) (0.097) (0.102) (0.102)
race_exog_pentWhite.AIAN -1.807*** -1.807*** -1.703*** -1.708***
  (0.033) (0.033) (0.033) (0.033)
race_exog_pentWhite.Black -2.668*** -2.651*** -2.654*** -2.646***
  (0.012) (0.012) (0.011) (0.011)
race_exog_pentWhite.E&SE Asian -1.784*** -1.531*** -1.196*** -1.103***
  (0.018) (0.019) (0.019) (0.020)
race_exog_pentWhite.Latino -1.611*** -1.486*** -1.058*** -1.030***
  (0.008) (0.009) (0.011) (0.011)
race_exog_pentWhite.South Asian -2.500*** -2.193*** -1.932*** -1.793***
  (0.057) (0.061) (0.065) (0.067)
bendog_partial_flex1.5TRUE   1.165***   0.724***
    (0.015)   (0.013)
language_endogTRUE     1.481*** 1.364***
      (0.010) (0.009)
deviance 1807364.220 1793966.535 1765898.716 1761544.387
***p < 0.001; **p < 0.01; *p < 0.05
Model predicting union formation on ACS 2014-18 data using specific Asian and Latino ethnic categories and Asian/Latino ethnic groups available in 1980
  base bendog lendog both
agediff 0.055*** 0.055*** 0.055*** 0.055***
  (0.000) (0.000) (0.000) (0.000)
agediff^2 -0.013*** -0.013*** -0.013*** -0.013***
  (0.000) (0.000) (0.000) (0.000)
hypergamyTRUE -0.129*** -0.138*** -0.131*** -0.135***
  (0.008) (0.009) (0.008) (0.008)
hypogamyTRUE 0.230*** 0.223*** 0.223*** 0.221***
  (0.009) (0.009) (0.009) (0.009)
edcross_hsTRUE -0.768*** -0.712*** -0.724*** -0.697***
  (0.010) (0.010) (0.010) (0.010)
edcross_scTRUE -0.666*** -0.656*** -0.658*** -0.654***
  (0.007) (0.007) (0.007) (0.007)
edcross_cTRUE -0.833*** -0.828*** -0.824*** -0.823***
  (0.007) (0.007) (0.007) (0.007)
race_exog_extendedAIAN.E&SE Asian -2.824*** -2.564*** -2.253*** -2.157***
  (0.184) (0.185) (0.183) (0.183)
race_exog_extendedAIAN.Latino -2.073*** -1.952*** -1.498*** -1.471***
  (0.050) (0.050) (0.051) (0.050)
race_exog_extendedAIAN.South Asian -3.047*** -2.759*** -2.398*** -2.268***
  (0.433) (0.429) (0.436) (0.432)
race_exog_extendedBlack.AIAN -2.560*** -2.542*** -2.458*** -2.453***
  (0.100) (0.100) (0.101) (0.101)
race_exog_extendedBlack.Chinese -4.370*** -4.077*** -3.625*** -3.515***
  (0.126) (0.126) (0.127) (0.127)
race_exog_extendedBlack.Cuban -3.123*** -2.854*** -2.374*** -2.274***
  (0.085) (0.087) (0.086) (0.086)
race_exog_extendedBlack.Filipino -2.993*** -2.699*** -2.526*** -2.415***
  (0.100) (0.098) (0.097) (0.097)
race_exog_extendedBlack.Japanese -2.800*** -2.591*** -2.478*** -2.387***
  (0.220) (0.220) (0.203) (0.205)
race_exog_extendedBlack.Korean -3.545*** -3.289*** -2.884*** -2.794***
  (0.156) (0.152) (0.150) (0.148)
race_exog_extendedBlack.Mexican -2.984*** -2.811*** -2.358*** -2.313***
  (0.030) (0.031) (0.033) (0.033)
race_exog_extendedBlack.Puerto Rican -1.870*** -1.772*** -1.337*** -1.317***
  (0.044) (0.044) (0.047) (0.047)
race_exog_extendedBlack.South Asian -4.152*** -3.830*** -3.590*** -3.436***
  (0.128) (0.129) (0.132) (0.132)
race_exog_extendedChinese.Filipino -1.813*** -1.449*** -0.946*** -0.803***
  (0.086) (0.087) (0.092) (0.093)
race_exog_extendedChinese.Japanese -0.689*** -0.361* 0.078 0.208
  (0.153) (0.165) (0.179) (0.184)
race_exog_extendedChinese.Korean -1.043*** -0.683*** -0.103 0.042
  (0.089) (0.093) (0.097) (0.100)
race_exog_extendedChinese.White -2.011*** -1.751*** -1.298*** -1.203***
  (0.028) (0.029) (0.030) (0.031)
race_exog_extendedCuban.Mexican -1.713*** -1.414*** -1.543*** -1.338***
  (0.079) (0.079) (0.079) (0.078)
race_exog_extendedCuban.Puerto Rican -1.100*** -0.844*** -1.017*** -0.841***
  (0.072) (0.073) (0.076) (0.077)
race_exog_extendedCuban.White -1.415*** -1.191*** -0.766*** -0.687***
  (0.036) (0.036) (0.034) (0.034)
race_exog_extendedE&SE Asian.South Asian -3.008*** -2.584*** -2.145*** -1.951***
  (0.105) (0.109) (0.120) (0.122)
race_exog_extendedFilipino.Japanese -1.366*** -1.046*** -0.856*** -0.718***
  (0.148) (0.152) (0.157) (0.158)
race_exog_extendedFilipino.Korean -1.793*** -1.423*** -0.962*** -0.816***
  (0.146) (0.160) (0.150) (0.157)
race_exog_extendedFilipino.White -1.629*** -1.360*** -1.189*** -1.087***
  (0.040) (0.041) (0.042) (0.044)
race_exog_extendedJapanese.Korean -0.743*** -0.442* -0.023 0.099
  (0.213) (0.204) (0.208) (0.207)
race_exog_extendedJapanese.White -1.229*** -1.053*** -0.944*** -0.871***
  (0.095) (0.099) (0.093) (0.097)
race_exog_extendedKorean.White -1.724*** -1.505*** -1.093*** -1.020***
  (0.054) (0.055) (0.055) (0.055)
race_exog_extendedLatino.E&SE Asian -3.035*** -2.726*** -2.110*** -1.995***
  (0.051) (0.052) (0.052) (0.052)
race_exog_extendedLatino.South Asian -3.440*** -3.089*** -2.557*** -2.406***
  (0.097) (0.097) (0.102) (0.102)
race_exog_extendedMexican.Puerto Rican -1.427*** -1.202*** -1.230*** -1.072***
  (0.046) (0.046) (0.049) (0.048)
race_exog_extendedMexican.White -1.649*** -1.521*** -1.095*** -1.067***
  (0.010) (0.010) (0.012) (0.012)
race_exog_extendedPuerto Rican.White -1.451*** -1.374*** -0.951*** -0.942***
  (0.024) (0.026) (0.025) (0.025)
race_exog_extendedWhite.AIAN -1.807*** -1.807*** -1.704*** -1.709***
  (0.033) (0.033) (0.033) (0.033)
race_exog_extendedWhite.Black -2.667*** -2.650*** -2.653*** -2.646***
  (0.012) (0.012) (0.012) (0.012)
race_exog_extendedWhite.South Asian -2.499*** -2.193*** -1.932*** -1.792***
  (0.057) (0.061) (0.065) (0.066)
race_filipino_latinoTRUE 0.856*** 0.867*** 0.693*** 0.700***
  (0.071) (0.071) (0.070) (0.070)
bendog_partial_flex1.5TRUE   1.162***   0.723***
    (0.015)   (0.013)
language_endogTRUE     1.477*** 1.361***
      (0.010) (0.009)
deviance 1806026.617 1792739.666 1764896.148 1760556.882
***p < 0.001; **p < 0.01; *p < 0.05
Model predicting union formation on ACS 2014-18 data using racial pentagon categories and all available Asian/Latino ethnic groups
  base bendog lendog both
agediff 0.054*** 0.054*** 0.053*** 0.053***
  (0.000) (0.000) (0.000) (0.000)
agediff^2 -0.012*** -0.012*** -0.012*** -0.012***
  (0.000) (0.000) (0.000) (0.000)
hypergamyTRUE -0.131*** -0.142*** -0.134*** -0.140***
  (0.009) (0.009) (0.009) (0.009)
hypogamyTRUE 0.216*** 0.208*** 0.209*** 0.205***
  (0.008) (0.008) (0.008) (0.008)
edcross_hsTRUE -0.777*** -0.712*** -0.732*** -0.698***
  (0.011) (0.010) (0.010) (0.010)
edcross_scTRUE -0.664*** -0.653*** -0.656*** -0.650***
  (0.006) (0.006) (0.006) (0.006)
edcross_cTRUE -0.826*** -0.820*** -0.816*** -0.814***
  (0.006) (0.006) (0.006) (0.006)
race_exog_pentAIAN.E&SE Asian -2.957*** -2.694*** -2.235*** -2.135***
  (0.154) (0.153) (0.155) (0.154)
race_exog_pentAIAN.Latino -2.131*** -1.990*** -1.521*** -1.480***
  (0.048) (0.048) (0.054) (0.054)
race_exog_pentAIAN.South Asian -3.305*** -2.977*** -2.652*** -2.493***
  (0.429) (0.427) (0.432) (0.429)
race_exog_pentBlack.AIAN -2.535*** -2.516*** -2.434*** -2.429***
  (0.075) (0.076) (0.077) (0.077)
race_exog_pentBlack.E&SE Asian -3.629*** -3.347*** -2.931*** -2.821***
  (0.050) (0.050) (0.051) (0.051)
race_exog_pentBlack.Latino -2.818*** -2.606*** -2.125*** -2.051***
  (0.019) (0.020) (0.020) (0.021)
race_exog_pentBlack.South Asian -4.134*** -3.822*** -3.520*** -3.372***
  (0.121) (0.124) (0.126) (0.127)
race_exog_pentE&SE Asian.E&SE Asian -1.310*** -0.951*** -0.373*** -0.220***
  (0.035) (0.035) (0.039) (0.038)
race_exog_pentE&SE Asian.South Asian -3.047*** -2.617*** -2.105*** -1.903***
  (0.087) (0.087) (0.088) (0.089)
race_exog_pentLatino.E&SE Asian -2.840*** -2.498*** -1.858*** -1.718***
  (0.029) (0.029) (0.031) (0.031)
race_exog_pentLatino.Latino -1.145*** -0.789*** -1.152*** -0.897***
  (0.014) (0.015) (0.016) (0.016)
race_exog_pentLatino.South Asian -3.529*** -3.142*** -2.562*** -2.382***
  (0.077) (0.076) (0.077) (0.077)
race_exog_pentSouth Asian.South Asian -1.307*** -0.831*** -0.642* -0.373
  (0.214) (0.203) (0.252) (0.236)
race_exog_pentWhite.AIAN -1.791*** -1.789*** -1.684*** -1.688***
  (0.034) (0.033) (0.035) (0.034)
race_exog_pentWhite.Black -2.681*** -2.663*** -2.667*** -2.659***
  (0.012) (0.012) (0.012) (0.012)
race_exog_pentWhite.E&SE Asian -1.951*** -1.702*** -1.276*** -1.182***
  (0.021) (0.024) (0.025) (0.027)
race_exog_pentWhite.Latino -1.699*** -1.530*** -1.082*** -1.029***
  (0.009) (0.010) (0.009) (0.010)
race_exog_pentWhite.South Asian -2.551*** -2.247*** -1.931*** -1.788***
  (0.054) (0.056) (0.056) (0.055)
bendog_partial_flex1.5TRUE   1.139***   0.736***
    (0.013)   (0.015)
language_endogTRUE     1.525*** 1.412***
      (0.008) (0.009)
deviance 1905362.424 1889663.413 1854765.654 1849202.413
***p < 0.001; **p < 0.01; *p < 0.05
Model predicting union formation on ACS 2014-18 data using specific Asian and Latino ethnic categories and largest available Asian/Latino ethnic groups
  base bendog lendog both
agediff 0.054*** 0.054*** 0.054*** 0.054***
  (0.000) (0.000) (0.000) (0.000)
agediff^2 -0.012*** -0.012*** -0.012*** -0.012***
  (0.000) (0.000) (0.000) (0.000)
hypergamyTRUE -0.133*** -0.144*** -0.135*** -0.140***
  (0.009) (0.009) (0.009) (0.009)
hypogamyTRUE 0.217*** 0.210*** 0.211*** 0.208***
  (0.008) (0.008) (0.008) (0.008)
edcross_hsTRUE -0.768*** -0.709*** -0.726*** -0.695***
  (0.009) (0.009) (0.009) (0.009)
edcross_scTRUE -0.661*** -0.650*** -0.653*** -0.648***
  (0.006) (0.006) (0.006) (0.006)
edcross_cTRUE -0.825*** -0.819*** -0.816*** -0.814***
  (0.007) (0.007) (0.007) (0.007)
race_exog_extendedAIAN.E&SE Asian -2.923*** -2.649*** -2.252*** -2.149***
  (0.169) (0.169) (0.169) (0.170)
race_exog_extendedAIAN.Latino -2.135*** -1.993*** -1.537*** -1.498***
  (0.046) (0.045) (0.046) (0.046)
race_exog_extendedAIAN.South Asian -3.310*** -3.012*** -2.636*** -2.501***
  (0.496) (0.495) (0.528) (0.527)
race_exog_extendedAsian Indian.Pakistani -1.113*** -0.689** -0.588* -0.346
  (0.231) (0.254) (0.268) (0.267)
race_exog_extendedBlack.AIAN -2.554*** -2.537*** -2.455*** -2.451***
  (0.073) (0.073) (0.073) (0.073)
race_exog_extendedBlack.Chinese -4.370*** -4.078*** -3.603*** -3.488***
  (0.127) (0.126) (0.128) (0.127)
race_exog_extendedBlack.Colombian -3.118*** -2.791*** -2.280*** -2.135***
  (0.125) (0.126) (0.129) (0.133)
race_exog_extendedBlack.Cuban -3.147*** -2.876*** -2.397*** -2.291***
  (0.086) (0.088) (0.086) (0.086)
race_exog_extendedBlack.Dominican -2.755*** -2.454*** -1.804*** -1.677***
  (0.081) (0.088) (0.084) (0.087)
race_exog_extendedBlack.Ecuadorian -3.756*** -3.336*** -2.822*** -2.628***
  (0.193) (0.194) (0.192) (0.192)
race_exog_extendedBlack.Filipino -3.009*** -2.707*** -2.537*** -2.415***
  (0.092) (0.098) (0.092) (0.093)
race_exog_extendedBlack.Guatemalan -4.270*** -3.744*** -3.283*** -3.018***
  (0.164) (0.166) (0.164) (0.164)
race_exog_extendedBlack.Japanese -2.760*** -2.567*** -2.411*** -2.325***
  (0.193) (0.186) (0.201) (0.195)
race_exog_extendedBlack.Korean -3.600*** -3.347*** -2.940*** -2.846***
  (0.149) (0.142) (0.149) (0.145)
race_exog_extendedBlack.Mexican -3.001*** -2.825*** -2.364*** -2.314***
  (0.028) (0.030) (0.030) (0.030)
race_exog_extendedBlack.Peruvian -3.229*** -2.827*** -2.362*** -2.179***
  (0.172) (0.170) (0.175) (0.173)
race_exog_extendedBlack.Puerto Rican -1.915*** -1.814*** -1.370*** -1.346***
  (0.040) (0.039) (0.042) (0.041)
race_exog_extendedBlack.Salvadorian -3.810*** -3.364*** -2.800*** -2.588***
  (0.108) (0.109) (0.113) (0.113)
race_exog_extendedBlack.South Asian -4.089*** -3.783*** -3.489*** -3.341***
  (0.118) (0.120) (0.117) (0.119)
race_exog_extendedBlack.Vietnamese -4.335*** -4.006*** -3.443*** -3.305***
  (0.169) (0.177) (0.183) (0.191)
race_exog_extendedChinese.Filipino -1.842*** -1.462*** -0.967*** -0.804***
  (0.086) (0.091) (0.088) (0.089)
race_exog_extendedChinese.Japanese -0.712*** -0.392** 0.052 0.189
  (0.135) (0.149) (0.131) (0.139)
race_exog_extendedChinese.Korean -1.010*** -0.641*** -0.054 0.104
  (0.083) (0.085) (0.087) (0.086)
race_exog_extendedChinese.Vietnamese -1.085*** -0.736*** -0.083 0.065
  (0.113) (0.120) (0.122) (0.123)
race_exog_extendedChinese.White -2.011*** -1.748*** -1.284*** -1.182***
  (0.031) (0.033) (0.029) (0.029)
race_exog_extendedColombian.Cuban -1.308*** -0.878*** -1.341*** -1.045***
  (0.116) (0.124) (0.124) (0.130)
race_exog_extendedColombian.Dominican -1.202*** -0.748*** -1.261*** -0.939***
  (0.131) (0.136) (0.140) (0.143)
race_exog_extendedColombian.Ecuadorian -0.657*** -0.136 -0.698*** -0.346
  (0.173) (0.176) (0.189) (0.190)
race_exog_extendedColombian.Guatemalan -1.695*** -1.091*** -1.726*** -1.305***
  (0.297) (0.312) (0.351) (0.354)
race_exog_extendedColombian.Mexican -1.307*** -0.958*** -1.224*** -0.976***
  (0.102) (0.099) (0.101) (0.099)
race_exog_extendedColombian.Peruvian -0.793** -0.250 -0.872*** -0.502*
  (0.269) (0.267) (0.257) (0.254)
race_exog_extendedColombian.Puerto Rican -0.972*** -0.655*** -0.895*** -0.672***
  (0.094) (0.094) (0.094) (0.093)
race_exog_extendedColombian.Salvadorian -1.328*** -0.795*** -1.344*** -0.977***
  (0.182) (0.183) (0.189) (0.191)
race_exog_extendedColombian.White -1.369*** -1.075*** -0.602*** -0.476***
  (0.045) (0.044) (0.043) (0.044)
race_exog_extendedCuban.Dominican -1.669*** -1.305*** -1.574*** -1.321***
  (0.128) (0.129) (0.134) (0.133)
race_exog_extendedCuban.Ecuadorian -1.401*** -0.908*** -1.315*** -0.979***
  (0.225) (0.236) (0.217) (0.218)
race_exog_extendedCuban.Guatemalan -2.780*** -2.173*** -2.738*** -2.317***
  (0.297) (0.297) (0.324) (0.321)
race_exog_extendedCuban.Mexican -1.757*** -1.454*** -1.604*** -1.388***
  (0.078) (0.080) (0.080) (0.080)
race_exog_extendedCuban.Peruvian -1.068*** -0.578** -1.093*** -0.751***
  (0.184) (0.199) (0.208) (0.221)
race_exog_extendedCuban.Puerto Rican -1.114*** -0.853*** -1.001*** -0.818***
  (0.083) (0.085) (0.080) (0.082)
race_exog_extendedCuban.Salvadorian -1.572*** -1.096*** -1.438*** -1.102***
  (0.167) (0.171) (0.170) (0.171)
race_exog_extendedCuban.White -1.424*** -1.207*** -0.782*** -0.702***
  (0.033) (0.032) (0.033) (0.033)
race_exog_extendedDominican.Ecuadorian -1.577*** -1.040*** -1.663*** -1.290***
  (0.147) (0.150) (0.151) (0.157)
race_exog_extendedDominican.Guatemalan -2.003*** -1.390*** -2.066*** -1.649***
  (0.197) (0.200) (0.200) (0.205)
race_exog_extendedDominican.Mexican -2.049*** -1.649*** -2.004*** -1.707***
  (0.123) (0.112) (0.125) (0.117)
race_exog_extendedDominican.Peruvian -1.481*** -0.979*** -1.541*** -1.196***
  (0.220) (0.219) (0.208) (0.209)
race_exog_extendedDominican.Puerto Rican -0.869*** -0.576*** -0.782*** -0.579***
  (0.086) (0.093) (0.093) (0.096)
race_exog_extendedDominican.Salvadorian -1.871*** -1.330*** -1.916*** -1.542***
  (0.160) (0.161) (0.162) (0.163)
race_exog_extendedDominican.White -2.581*** -2.316*** -1.663*** -1.556***
  (0.069) (0.070) (0.070) (0.069)
race_exog_extendedEcuadorian.Guatemalan -1.504*** -0.842** -1.566*** -1.121***
  (0.262) (0.269) (0.264) (0.267)
race_exog_extendedEcuadorian.Mexican -1.300*** -0.844*** -1.306*** -0.984***
  (0.119) (0.118) (0.111) (0.113)
race_exog_extendedEcuadorian.Peruvian -0.742* -0.161 -0.831* -0.440
  (0.365) (0.352) (0.346) (0.353)
race_exog_extendedEcuadorian.Puerto Rican -1.456*** -1.038*** -1.363*** -1.079***
  (0.121) (0.120) (0.121) (0.121)
race_exog_extendedEcuadorian.Salvadorian -1.133*** -0.510** -1.183*** -0.756***
  (0.192) (0.191) (0.193) (0.193)
race_exog_extendedEcuadorian.White -1.962*** -1.584*** -1.108*** -0.937***
  (0.075) (0.076) (0.080) (0.081)
race_exog_extendedE&SE Asian.South Asian -3.020*** -2.595*** -2.113*** -1.909***
  (0.094) (0.096) (0.100) (0.101)
race_exog_extendedFilipino.Japanese -1.430*** -1.109*** -0.900*** -0.761***
  (0.155) (0.159) (0.166) (0.168)
race_exog_extendedFilipino.Korean -1.790*** -1.423*** -0.949*** -0.792***
  (0.208) (0.193) (0.210) (0.206)
race_exog_extendedFilipino.Vietnamese -1.609*** -1.212*** -0.682*** -0.508**
  (0.139) (0.151) (0.157) (0.166)
race_exog_extendedFilipino.White -1.638*** -1.368*** -1.201*** -1.094***
  (0.037) (0.037) (0.037) (0.037)
race_exog_extendedGuatemalan.Mexican -1.094*** -0.575*** -1.110*** -0.753***
  (0.056) (0.054) (0.055) (0.053)
race_exog_extendedGuatemalan.Peruvian -1.433*** -0.792** -1.470*** -1.034***
  (0.267) (0.276) (0.274) (0.278)
race_exog_extendedGuatemalan.Puerto Rican -1.854*** -1.333*** -1.648*** -1.294***
  (0.166) (0.160) (0.154) (0.150)
race_exog_extendedGuatemalan.Salvadorian -0.438*** 0.214 -0.524*** -0.086
  (0.105) (0.120) (0.112) (0.124)
race_exog_extendedGuatemalan.White -2.729*** -2.241*** -1.786*** -1.541***
  (0.070) (0.067) (0.089) (0.085)
race_exog_extendedJapanese.Korean -0.877*** -0.537* -0.095 0.057
  (0.227) (0.222) (0.221) (0.219)
race_exog_extendedJapanese.Vietnamese -1.616*** -1.242*** -0.674** -0.516*
  (0.248) (0.255) (0.257) (0.260)
race_exog_extendedJapanese.White -1.230*** -1.050*** -0.944*** -0.863***
  (0.065) (0.061) (0.064) (0.062)
race_exog_extendedKorean.Vietnamese -1.685*** -1.272*** -0.602*** -0.420*
  (0.163) (0.164) (0.171) (0.170)
race_exog_extendedKorean.White -1.739*** -1.517*** -1.116*** -1.035***
  (0.064) (0.062) (0.059) (0.058)
race_exog_extendedLatino.E&SE Asian -3.189*** -2.828*** -2.174*** -2.024***
  (0.042) (0.042) (0.041) (0.041)
race_exog_extendedLatino.South Asian -3.527*** -3.153*** -2.580*** -2.406***
  (0.083) (0.084) (0.082) (0.082)
race_exog_extendedMexican.Peruvian -1.246*** -0.856*** -1.179*** -0.905***
  (0.094) (0.094) (0.099) (0.101)
race_exog_extendedMexican.Puerto Rican -1.443*** -1.215*** -1.225*** -1.067***
  (0.043) (0.048) (0.050) (0.055)
race_exog_extendedMexican.Salvadorian -1.131*** -0.688*** -1.133*** -0.826***
  (0.043) (0.043) (0.046) (0.047)
race_exog_extendedMexican.White -1.665*** -1.533*** -1.103*** -1.070***
  (0.009) (0.009) (0.010) (0.010)
race_exog_extendedPeruvian.Puerto Rican -1.157*** -0.756*** -1.094*** -0.808***
  (0.151) (0.164) (0.159) (0.165)
race_exog_extendedPeruvian.Salvadorian -1.164*** -0.627** -1.238*** -0.872***
  (0.196) (0.204) (0.195) (0.201)
race_exog_extendedPeruvian.White -1.397*** -1.040*** -0.617*** -0.452***
  (0.067) (0.063) (0.065) (0.063)
race_exog_extendedPuerto Rican.Salvadorian -1.775*** -1.336*** -1.621*** -1.327***
  (0.112) (0.118) (0.117) (0.120)
race_exog_extendedPuerto Rican.White -1.486*** -1.406*** -0.979*** -0.968***
  (0.020) (0.021) (0.022) (0.022)
race_exog_extendedSalvadorian.White -2.637*** -2.235*** -1.704*** -1.514***
  (0.056) (0.054) (0.060) (0.060)
race_exog_extendedVietnamese.White -2.398*** -2.101*** -1.519*** -1.404***
  (0.050) (0.051) (0.055) (0.057)
race_exog_extendedWhite.AIAN -1.805*** -1.805*** -1.702*** -1.707***
  (0.030) (0.031) (0.032) (0.032)
race_exog_extendedWhite.Black -2.673*** -2.656*** -2.659*** -2.651***
  (0.014) (0.015) (0.015) (0.015)
race_exog_extendedWhite.South Asian -2.546*** -2.251*** -1.949*** -1.810***
  (0.044) (0.047) (0.046) (0.047)
race_filipino_latinoTRUE 0.946*** 0.943*** 0.753*** 0.751***
  (0.059) (0.060) (0.060) (0.060)
bendog_partial_flex1.5TRUE   1.152***   0.748***
    (0.013)   (0.014)
language_endogTRUE     1.495*** 1.380***
      (0.008) (0.008)
deviance 1866010.382 1851164.574 1819995.291 1814661.078
***p < 0.001; **p < 0.01; *p < 0.05